{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6d96e659-f743-40cb-bb49-d916a215aa4f",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Options Strategies, Portfolio Optimization and Risk Management"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bfc0241-b312-45cd-b1b6-cfecab9ad85e",
   "metadata": {},
   "source": [
    "# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d0d0add-ff03-4c37-af3c-3942c8adc582",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Load the Libs we need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4b0d19b3-7892-470c-8051-b3460605ed1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import Lib\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import pytz\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import yfinance as yf\n",
    "import scipy.stats as si\n",
    "import math\n",
    "import networkx as nx\n",
    "\n",
    "# import module\n",
    "from datetime import datetime, timezone\n",
    "from datetime import date, time\n",
    "from math import trunc\n",
    "from dateutil.parser import parse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36335255-3a12-4108-b282-d524a5e889cb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f11d6120-4799-417d-a247-2165b95bfa7b",
   "metadata": {},
   "source": [
    "## Introduction to Portfolio Optimization and Risk Management"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f4c7d556-5ca3-45e3-8c81-e34722ac039c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Portfolio Expected Return: 0.10\n",
      "Portfolio Standard Deviation: 0.18\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Suppose we have two AAPL options with the following expected returns and standard deviations:\n",
    "# Option1: Expected Return = 0.08, Standard Deviation = 0.2\n",
    "# Option2: Expected Return = 0.12, Standard Deviation = 0.3\n",
    "\n",
    "# Portfolio weights (50% in each option)\n",
    "weights = np.array([0.5, 0.5])\n",
    "\n",
    "# Expected returns and standard deviations\n",
    "returns = np.array([0.08, 0.12])\n",
    "std_devs = np.array([0.2, 0.3])\n",
    "\n",
    "# Calculate portfolio expected return and standard deviation\n",
    "port_return = np.dot(weights, returns)\n",
    "port_std_dev = np.sqrt(np.dot(weights**2, std_devs**2))\n",
    "\n",
    "print(f\"Portfolio Expected Return: {port_return:.2f}\")\n",
    "print(f\"Portfolio Standard Deviation: {port_std_dev:.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ebd68e29-5709-4c11-b414-c3fee9c5bc45",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal Weights: [0.59920317 0.40079683]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.optimize import minimize\n",
    "\n",
    "# Sample returns and covariance matrix for two AAPL options\n",
    "returns = np.array([0.08, 0.12])\n",
    "cov_matrix = np.array([[0.04, 0.02], [0.02, 0.09]])  # Assuming some covariance between the options\n",
    "\n",
    "def negative_sharpe(weights): \n",
    "    port_return = np.dot(weights, returns)\n",
    "    port_volatility = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights)))\n",
    "    return -port_return / port_volatility\n",
    "\n",
    "# Constraints and bounds\n",
    "cons = ({'type': 'eq', 'fun': lambda weights: np.sum(weights) - 1}) \n",
    "bounds = ((0, 1), (0, 1))\n",
    "initial_weights = [0.5, 0.5]\n",
    "\n",
    "solution = minimize(negative_sharpe, initial_weights, method='SLSQP', bounds=bounds, constraints=cons)\n",
    "optimal_weights = solution.x\n",
    "\n",
    "print(f\"Optimal Weights: {optimal_weights}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5eec107-2124-4fea-ac87-02a253b24409",
   "metadata": {},
   "source": [
    "## Diversification and Modern Portfolio Theory in Options Trading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "947b4819-0387-4b5d-b27b-7afe28e44b77",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Portfolio Standard Deviation with Diversification: 0.13\n"
     ]
    }
   ],
   "source": [
    "# Assuming a correlation of -0.5 between the two options\n",
    "correlation = -0.5\n",
    "covariance = correlation * std_devs[0] * std_devs[1]\n",
    "\n",
    "# Portfolio variance formula considering covariance\n",
    "port_variance = weights[0]**2 * std_devs[0]**2 + weights[1]**2 * std_devs[1]**2 + 2 * weights[0] * weights[1] * covariance\n",
    "port_std_dev_diversified = np.sqrt(port_variance)\n",
    "\n",
    "print(f\"Portfolio Standard Deviation with Diversification: {port_std_dev_diversified:.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e5b216c5-e541-479b-9c58-7a560d1a593d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "num_portfolios = 10000\n",
    "results = np.zeros((3, num_portfolios))\n",
    "for i in range(num_portfolios):\n",
    "    weights = np.random.random(2)\n",
    "    weights /= np.sum(weights)\n",
    "    \n",
    "    port_return = np.dot(weights, returns)\n",
    "    port_volatility = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights)))\n",
    "    results[0,i] = port_return\n",
    "    results[1,i] = port_volatility\n",
    "    results[2,i] = results[0,i] / results[1,i]  # Sharpe Ratio\n",
    "\n",
    "# Plotting\n",
    "plt.scatter(results[1,:], results[0,:], c=results[2,:], cmap='YlGnBu', marker='o')\n",
    "plt.title('Efficient Frontier with AAPL Options')\n",
    "plt.xlabel('Portfolio Volatility')\n",
    "plt.ylabel('Portfolio Return')\n",
    "plt.colorbar(label='Sharpe Ratio')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebf71794-7cdf-4c08-bf65-a10651437248",
   "metadata": {},
   "source": [
    "## Constructing Efficient Portfolios with Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c821a0e2-4696-48e0-b543-9484ff6efd7c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Option  Price  Expected_Return  Weight  Investment\n",
      "0  AAPL_Call_150     10             0.08     0.5         5.0\n",
      "1   AAPL_Put_140      8             0.12     0.5         4.0\n",
      "Total Portfolio Cost: $9.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# For simplicity, let's assume we already have a dataframe with AAPL options data\n",
    "# df_options = pd.read_csv('AAPL_options_data.csv')\n",
    "\n",
    "# Sample DataFrame (Replace this with actual data)\n",
    "data = {\n",
    "    'Option': ['AAPL_Call_150', 'AAPL_Put_140'],\n",
    "    'Price': [10, 8],\n",
    "    'Expected_Return': [0.08, 0.12]\n",
    "}\n",
    "df_options = pd.DataFrame(data)\n",
    "\n",
    "# Create a portfolio with equal weight to each option\n",
    "df_options['Weight'] = 0.5\n",
    "\n",
    "# Calculate portfolio cost\n",
    "df_options['Investment'] = df_options['Weight'] * df_options['Price']\n",
    "portfolio_cost = df_options['Investment'].sum()\n",
    "\n",
    "print(df_options)\n",
    "print(f\"Total Portfolio Cost: ${portfolio_cost}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "36073d14-2f38-4be7-bd7a-4ad651205d88",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal Weights: [4.16333634e-17 4.68375339e-16 1.00000000e+00]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.optimize import minimize\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Mocking a dataset of AAPL options with historical returns\n",
    "data = {\n",
    "    'AAPL_Call_150': [0.02, 0.03, 0.04, 0.03, 0.05],\n",
    "    'AAPL_Put_140': [-0.01, -0.02, 0.02, 0.03, 0.01],\n",
    "    'AAPL_Call_155': [0.04, 0.05, 0.06, 0.07, 0.08]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Calculate mean returns and covariance matrix\n",
    "mean_returns = df.mean()\n",
    "cov_matrix = df.cov()\n",
    "\n",
    "# Objective function to maximize the Sharpe Ratio\n",
    "def negative_sharpe(weights): \n",
    "    port_return = np.dot(weights, mean_returns)\n",
    "    port_volatility = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights)))\n",
    "    return -port_return / port_volatility\n",
    "\n",
    "# Constraints and bounds for three assets\n",
    "cons = ({'type': 'eq', 'fun': lambda weights: np.sum(weights) - 1}) \n",
    "bounds = ((0, 1), (0, 1), (0, 1))\n",
    "initial_weights = [1/3, 1/3, 1/3]\n",
    "\n",
    "# Optimization to find the optimal weights\n",
    "solution = minimize(negative_sharpe, initial_weights, method='SLSQP', bounds=bounds, constraints=cons)\n",
    "optimal_weights = solution.x\n",
    "\n",
    "print(f\"Optimal Weights: {optimal_weights}\")\n",
    "\n",
    "# Visualize Efficient Frontier\n",
    "portfolio_returns = []\n",
    "portfolio_volatilities = []\n",
    "for _ in range(10000):\n",
    "    weights = np.random.random(3)\n",
    "    weights /= np.sum(weights)\n",
    "    \n",
    "    port_return = np.dot(weights, mean_returns)\n",
    "    port_vol = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights)))\n",
    "    \n",
    "    portfolio_returns.append(port_return)\n",
    "    portfolio_volatilities.append(port_vol)\n",
    "\n",
    "plt.scatter(portfolio_volatilities, portfolio_returns, c=[r/v for r, v in zip(portfolio_returns, portfolio_volatilities)], cmap='YlGnBu', marker='o')\n",
    "plt.title('Efficient Frontier of AAPL Options Portfolio')\n",
    "plt.xlabel('Portfolio Volatility')\n",
    "plt.ylabel('Portfolio Return')\n",
    "plt.colorbar(label='Sharpe Ratio')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0d1abdc-ce33-41c6-b40d-b2341c282f1c",
   "metadata": {},
   "source": [
    "## Options Strategies and Risk Management Techniques in Options Trading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "06fefa0b-f337-45f2-b760-03c2011a36f5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Profit if Option Not Exercised: $5\n",
      "Profit if Option Exercised: $10\n"
     ]
    }
   ],
   "source": [
    "# Assuming we own AAPL stock and we write/sell a call option\n",
    "stock_price = 145\n",
    "call_strike = 150\n",
    "call_premium = 5\n",
    "\n",
    "# Profit if option is not exercised\n",
    "profit_not_exercised = call_premium\n",
    "\n",
    "# Profit if option is exercised\n",
    "profit_exercised = call_premium + (call_strike - stock_price)\n",
    "\n",
    "print(f\"Profit if Option Not Exercised: ${profit_not_exercised}\")\n",
    "print(f\"Profit if Option Exercised: ${profit_exercised}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "17454ad0-2de1-4915-a9e1-6924554f3ded",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Butterfly Spread: Long 1 lower strike, Short 2 middle strike, Long 1 higher strike\n",
    "lower_strike = 140\n",
    "middle_strike = 145\n",
    "higher_strike = 150\n",
    "\n",
    "premium_lower = 5\n",
    "premium_middle = 7\n",
    "premium_higher = 3\n",
    "\n",
    "def butterfly_payoff(stock_price):\n",
    "    return (max(stock_price - lower_strike, 0) - 2 * max(stock_price - middle_strike, 0) \n",
    "            + max(stock_price - higher_strike, 0) - premium_lower + 2 * premium_middle - premium_higher)\n",
    "\n",
    "stock_prices = np.arange(135, 155, 1)\n",
    "payoffs = [butterfly_payoff(s) for s in stock_prices]\n",
    "\n",
    "plt.plot(stock_prices, payoffs)\n",
    "plt.title('Butterfly Spread Payoff for AAPL Options')\n",
    "plt.xlabel('AAPL Stock Price at Expiry')\n",
    "plt.ylabel('Profit/Loss')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc5e03df-8eb6-41f3-bda0-c02938466c5c",
   "metadata": {},
   "source": [
    "## Integrating Risk Management into Trading Strategies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "686fff94-a522-455f-b083-189b012f25dd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value at Risk at 95% Confidence Level: $-0.20\n"
     ]
    }
   ],
   "source": [
    "# Assuming portfolio follows a normal distribution\n",
    "# For simplicity, using the previously calculated portfolio standard deviation (port_std_dev)\n",
    "\n",
    "confidence_level = 0.95\n",
    "from scipy.stats import norm\n",
    "\n",
    "VaR = port_return - norm.ppf(confidence_level) * port_std_dev\n",
    "\n",
    "print(f\"Value at Risk at 95% Confidence Level: ${VaR:.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1ea0ad5a-fb61-4f77-b950-b06c711c1bba",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value at Risk (VaR) at 95% Confidence Level: $-0.26\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.optimize import minimize\n",
    "import pandas as pd\n",
    "\n",
    "np.random.seed(42)  # for reproducibility\n",
    "\n",
    "# Mock returns and covariance matrix for three AAPL options\n",
    "returns = np.array([0.08, 0.12, 0.07])\n",
    "cov_matrix = np.array([[0.04, 0.02, 0.01], \n",
    "                       [0.02, 0.09, 0.03], \n",
    "                       [0.01, 0.03, 0.05]]) \n",
    "\n",
    "# Objective function to maximize the Sharpe Ratio\n",
    "def negative_sharpe(weights): \n",
    "    port_return = np.dot(weights, returns)\n",
    "    port_volatility = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights)))\n",
    "    return -port_return / port_volatility\n",
    "\n",
    "# Constraints and bounds for three options\n",
    "cons = ({'type': 'eq', 'fun': lambda weights: np.sum(weights) - 1}) \n",
    "bounds = ((0, 1), (0, 1), (0, 1))\n",
    "initial_weights = [1/3, 1/3, 1/3]  # Evenly distributed for the start\n",
    "\n",
    "# Optimization\n",
    "solution = minimize(negative_sharpe, initial_weights, method='SLSQP', bounds=bounds, constraints=cons)\n",
    "optimal_weights = solution.x\n",
    "\n",
    "# Monte Carlo Simulation for VaR\n",
    "num_simulations = 10000\n",
    "simulation_results = np.zeros(num_simulations)\n",
    "\n",
    "for i in range(num_simulations):\n",
    "    random_returns = np.random.multivariate_normal(mean_returns, cov_matrix)\n",
    "    portfolio_return = np.dot(optimal_weights, random_returns)\n",
    "    simulation_results[i] = portfolio_return\n",
    "\n",
    "# Calculate VaR at 95% confidence level\n",
    "VaR_95 = np.percentile(simulation_results, 5)\n",
    "print(f\"Value at Risk (VaR) at 95% Confidence Level: ${VaR_95:.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d04fef06-8090-4fe4-a16d-85cf90b566eb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adf1f2af-5ce9-4c12-9472-f21ccc65d183",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2cec22ec-adb6-4dd1-8f40-4976a6c10c7d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10f9d8b5-0cc2-4790-bed2-fe09e84e4902",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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